Cerebral autoregulation (CAR) is a term commonly used to describe the pressure autoregulation mechanism that adjusts cerebrovascular resistance (tone) in response to blood pressure changes. It is thought that this mechanism acts to maintain adequate cerebral blood flow (CBF) over a wide range of arterial blood pressure in healthy subjects.
Various Cerebral autoregulatory mechanisms, including CAR, are influenced by various processes that operate on independent timescales. Studies have provided support for the belief that autonomic (neurogenic) control of cerebral vasculature is a primary factor in maintaining homeostatic cerebral blood flow, specifically in the arterial blood pressure range where cerebrovascular control mechanisms are most active in healthy individuals. These studies have also provided support for the belief that dysfunction of neurogenic control in individuals with mild or concussive brain injury could be responsible for clinical symptoms and the known risks of subsequent traumatic injury, even when arterial blood pressure is within a normal range.
Evidence suggests that dysfunction of CAR leads to overpressure of the brain and pathological injury when arterial blood pressure rises, as for example during exercise. Dysfunction of CAR can also lead to under perfusion of the brain when arterial blood pressure falls, thus causing ischemic injury.
Studies have shown that when the CAR mechanism is dysfunctional, CBF variations exhibit passive, synchronized behavior with respect to steady state arterial blood pressure changes.
There are at least three spontaneous, normally occurring blood pressure variations in the brain, including: 1) variations corresponding to heart rate, 2) variations corresponding to respiration, and 3) nonstationary “slow wave” variations with periods typically over 30 seconds. The first two are relatively cyclic, provided heart rate and respiration rate are constant. The slow wave variations are seldom cyclic with constant period, and their genesis is not well understood.
Spontaneous normally occurring CBF variations include the same three categories as blood pressure. CBF waveforms at heart rate and respiratory frequencies are closely linked to corresponding arterial pressure waveforms. It is Applicant's viewpoint that, CBF slow wave variations are normally not closely linked to blood pressure, and exhibit low coherence to blood pressure in healthy subjects.
It is known to Applicant and others that in addition to the said three types of recurring CBF variations, certain episodic events including visual stimuli and cognitive exertion may produce significant CBF changes without corresponding arterial blood pressure changes.
Other cerebral autoregulatory mechanisms operate independently to adjust cerebral blood flow up or down in response to changes in arterial carbon dioxide and cerebral metabolic needs. Cerebral blood flow therefore varies spontaneously by several percent over time scales corresponding to arterial carbon dioxide fluctuations, which can be 30-120 seconds typically. Spontaneous variations in cerebral blood flow caused by these mechanisms, which are independent of arterial pressure, are therefore not synchronous with, or correlated with, spontaneous arterial pressure fluctuations. These mechanisms confound a straight forward analysis of potential CAR dysfunction.
CBF variations naturally produce synchronized variations in the concentrations of oxy-hemoglobin, deoxy-hemoglobin, and total hemoglobin in a region of tissue. These parameters can be measured noninvasively using near infrared spectroscopy (NIRS). Regional oxygen saturation (rSO2), defined as the ratio of oxy-Hb to total hemoglobin, and other NIRS-derived parameters have been shown to closely correlate with dynamic CBF changes over time scales of interest for CAR assessments
The present disclosure is related to methods for evaluation of spontaneous dynamic CBF and arterial pressure changes to determine if the CAR mechanism of a patient is dysfunctional. More specifically, it relates to time-domain measurements of lag time of waveforms of one parameter with respect to the other. The methods described are tolerant of nonstationary variations in the parameters, and therefore do not require stationary data.